Abstract: A learning device includes an acquisition unit that acquires first learning data as information indicating at least one of weather at a first spot at a plurality of times and congestion information as information regarding congestion, a normal prediction learned model that outputs a number of people in normal times at the first spot at a certain time when first information indicating at least one of the congestion information and the weather at the first spot at the certain time is inputted thereto, and true value information including true values indicating the numbers of people in normal times and in emergency times at the first spot at a plurality of times, a learning generation unit that generates an emergency prediction learned model that outputs the number of people in emergency times at the first spot at the certain time when the first information is inputted thereto by using the first learning data, the normal prediction learned model and the true value information, and an output unit that outputs the

Abstract: An information processing device includes a data collecting unit that collects sensor data from a plurality of sensors; a determination-data generating unit that generates determination batch data including learned data and unlearned data, the learned data being learning data that has already been used to learn a learning model for making a prediction based on the sensor data, the unlearned data corresponding to the sensor data; and a relearning determining unit that calculates propensity scores for the learned data and the unlearned data by using a covariate affecting a result of the prediction to perform stratification by allocating the learned data and the unlearned data to a plurality of layers, and to determine whether or not the learning model is to be relearned by using a frequency of appearance of a critical layer and a frequency of appearance of critical data from a result of the stratification.